Orthogonal Frequency Division Multiplex (OFDM) is used for the air interface for the IEEE 802.11a, ETSI Hiperian-II, terrestrial digital video broadcast (DVB-T), and Broadband Wireless Internet Forum (BWIF) standards. It is also used in wireline transmission, notably Asynchronous Digital Subscriber Lines (ADSL). The OFDM technique sends many carriers (sometimes thousands of carriers) in parallel on adjacent frequencies within a frequency band. The frequencies are variously called frequency “bins”, tones, or subbands. Tones is the term used in the following description.
The OFDM technique relies on the orthogonality properties of the fast Fourier transform (FFT) and the inverse fast Fourier transform (IFFT) to eliminate interference between the carriers. The FFT can be thought of as a bank of filters, with each filter having the identical frequency response as the other filters, but centered on different center frequencies. The response of each filter in the filter bank is a maximum at the center frequency of the filter, but zero at the center frequencies of the other filters. Thus, if the carriers are centered on the center frequencies of the filters, intercarrier interference is eliminated. If a carrier frequency is not perfectly centered, then its frequency is not at the zero response points of the FFT filters, and some of the carrier energy appears in the FFT outputs at the other frequencies. In FFT nomenclature, this is called leakage. Leakage is the main source of intercarrier interference in OFDM systems.
At the transmitter, the precise setting of the carrier frequencies is performed by an IFFT. The data is encoded into constellation points by multiple (one for each carrier) constellation encoders. The complex values of the constellation encoder outputs are the inputs to the IFFT. For wireless transmission, the outputs of the IFFT are converted to an analog waveform, unconverted to a radio frequency, amplified, and transmitted.
At the receiver, the reverse process is performed. The received signal is amplified, downconverted to a band suitable for analog to digital conversion, digitized, and ultimately processed by a FFT to recover the carriers. The multiple carriers are then demodulated in multiple constellation decoders (one for each carrier), recovering the original data.
Since an IFFT is used to combine the carriers at the transmitter and a corresponding FFT is used to separate the carriers at the receiver, the process has potentially zero intercarrier interference (ICI). However, practicalities such as synchronization errors, frequency tracking errors, and phase noise introduce intercarrier interference by shifting or jittering the carriers from the center frequencies of the filters and allowing some leakage. Some of these effects can be compensated, and some cannot. This patent is concerned with compensation methods for phase noise degradations.
In wireless transmissions, the signal can be reflected or scattered from buildings, vehicles, trees, vegetation, and terrain features. Multiple copies of the signal, each with a different time delay that depends upon the path traversed, are summed at the receive antenna, This phenomenon is called multipath transmission, or more simply multipath. Multipath causes fading and attenuation in the frequency band, which if uncompensated would cause unacceptably large numbers of errors in the decoding process.
To avoid this, carriers with known amplitude and phase are transmitted for the purpose of measuring the wireless transmission channel. These carriers are known as training tones or pilot tones. In some systems, they are transmitted in a separate burst composed entirely of training tones. In other systems, such as the BWIF standardized system to be described herein, they are interspersed among the data carriers (data tones.) In either case, since the training tones are known apriori, the response of the wireless channel at the training tone frequencies is easily determined. Once this is done, the channel responses at the data tone frequencies can be interpolated from the known responses at the training tone frequencies. This process is called channel estimation. The measured and interpolated channel responses, known as channel estimates, are used in the decoding process to minimize decoding errors.
Phase noise is introduced into the signals by phase noise in the local oscillators used to upconvert and downconvert the signal, as well as by timing jitter of the sampling in the digital to analog and analog to digital converters at the transmitter and receiver, respectively. Each carrier, which would have a line spectrum otherwise, now has a phase noise spectrum associated with it.
For OFDM systems, phase noise can be classified into two categories—common phase noise and foreign phase noise. Common phase noise is so named because it affects all carriers equally. It is generated by the local oscillators' phase noises and the data converters' timing errors, which modulate each carrier equally. This causes a phase rotation on each and every carrier that is equal in sign and amount. Foreign phase noise, on the other hand, is caused by the leakage of the phase noise spectra of the other carriers (“foreign” carriers) into the filter response of a given carrier. Foreign phase noise appears as random noise from carrier to carrier. The effects of phase noise on OFDM systems are analyzed in the article “Influence of RF Oscillators on an OFDM Signal”, by Claus Muschallik. This was published in the IEEE Transactions on Consumer Electronics, Vol. 41, No. 3, August 1995, pp. 592–602.
FIG. 1 illustrates a functional block diagram of a wireless receiver system 10 employing a channel estimator 20. A data signal or burst is received by an antenna 14, which picks up the data signal and couples the data signal to front end processing 12. The data signal or burst can include training and calibration information. The processing 12 amplifies the data signal, converts the data signal to an IF frequency and filters the data signal to remove signal outside the desired frequency band. The front end processing 12 is coupled to one or more analog-to-digital converters 16 that sample the data signal and provide digitized signal output. The digitized signals of the one or more analog to digital converters are provided to a digital preprocessor 18. The digital preprocessor then performs a FFT on the digitized signal. The FFT on the digitized signal converts the signal from the time domain to the frequency domain so that the frequencies or tones carrying the data can be provided. The frequencies and tones can then be demodulated or decoded. The demodulation of the tones requires information relating to the wireless channel magnitude and phase at each tone. The effects of the dispersion caused by the channel needs to be compensated prior to decoding of the signal, so that decoding errors can be minimized. A channel estimator 20 is provided to determine the amount of phase rotation and magnitude perturbation applied to the tones by the channel. Since the training tones are transmitted with known magnitude and phase, the channel response at the training tones is easily determined. The known channel response at the training tones is then interpolated in the frequency domain to determine the channel response at the data tones. A cyclic interpolation procedure can be employed. The channel estimate is then provided to the data demodulator 22 for demodulation of the digital data signal. The demodulated data signal is then transmitted to a data postprocessing block 24 for further signal processing. The data post processing performs error correction utilizing the information from the data demodulator 22. The channel estimator 20 extracts the training tones from the number of tones in the data signal or data burst and performs several signal processing steps on the training tones. After correcting for any transmitted magnitude and phase differences, the channel estimator 20 performs an IFFT on the training tones to provide a channel impulse response. The channel impulse response will eventually be zero padded and have a FFT performed on it to obtain channel estimate values at frequencies between the training tone frequencies.
The BWIF channel estimation method is shown in FIG. 2. The procedure starts by providing a set of frequency samples of training tones H(k) and at block 31 extracting the training (also called pilot) tones HTT(k) from the set of all tones H. Note that N denotes the number of all tones, and NTT denotes the number of training tones. Modulation on the training tones is removed at block 32, and the edge tone, which is transmitted at reduced amplitude, is re-scaled to proper amplitude at step 33. In block 34, an inverse fast Fourier transform (IFFT) on the training tones is performed to obtain the channel impulse response. The impulse response is scaled to unity in block 35. In block 36 the impulse response is averaged with previous bursts to reduce noise. The average impulse response is then zero padded (block 37) and an N-point FFT is performed at block 38 to produce the final channel estimate. Note that it is possible to omit the averager at block 36 (or equivalently, set α equal to zero in the averaging equation in block 36).
Common phase noise causes all of the tones (H and HTT) to be phase shifted by a common amount. Therefore, every term of the impulse response is also phase shifted by the same amount. This phase shift is a zero mean random variable and changes for each burst. The problem occurs at block 36 of FIG. 1, when multiple impulse responses are averaged to produce an average impulse response. The averaging is intended to reduce the noise in the final estimate. However, it also averages the common phase noise to near zero. This can be a problem in that the data tones will have the common phase shift, but the channel estimate produced from the average impulse response will not. This results in increased error rates in the demodulation of the data. For the standard BWIF processing, this is only a problem if the averager is enabled (α≠0). If the averager is not enabled, the common phase noise is present identically in both the channel estimate and the data, and is thus compensated in the standard demodulation process.
In addition to this problem, Texas Instruments has developed (see Appendix A; TI-32977, Ser. No. 10/001,986 filed Oct. 31, 2001) a computationally more efficient method of obtaining channel estimates for the BWIF system. This application is incorporated herein by reference. The TI method estimates 1/K of the tones (typically ¼ of the tones) at each burst, and uses K bursts (typically 4 bursts) to estimate the tones. At each burst, a channel estimate for all tones is obtained by using the N/K tone estimates computed on the current burst, and the (K−1)*N/K tone estimates from the (K−1) previous bursts.
Phase noise causes two problems here. First, any impulse response averaging will average the phase noise to zero, just as in the original BWIF method. Since the data will be phase shifted (by the common phase noise), but the channel estimate is not, there is an error introduced in the demodulation of the data, which causes increased error rates. Secondly, since the complete channel estimate is formed over K bursts, each N/K set of estimates was obtained at a different phase state (from the common phase noise). This makes the channel estimates obtained with this method noisy.